Conversion of Human Neocortical Neurosolver model to NetPyNE

The goal of Human Neocortical Neurosolver (HNN) project is to create a new software tool that gives researchers the ability to develop hypotheses on the location, time-course and circuit-level neural mechanisms underlying human Magneto/Electro-encephalography (MEG/EEG) and ECoG signals. The purpose of the subaward contract with SUNY Downstate is to make use of a unique neuronal network modeling language specification and programming interface (NetPyNE) to substantially increase the utility of the HNN software. The goal is to  convert HNN's neocortical model into the NetPyNE language, allowing for user-friendly, modular specification of HNN's models for enhanced use by the research community. 

Utilizing NetPyNE will also allow test-case users to adapt microcircuit architecture more easily to match the needs of the specific neocortical area they are working with, whether they are exploring auditory, visual, or motor areas. This will be important particularly for motor cortex which lacks a pronounced granular layer, in contrast with somatosensory cortex. In addition, users will be able to add different cell types from existing NEURON models and integrate them with HNN’s neocortical models. Importantly, users will be able to investigate how the highly complex dendritic geometry influences the generation of MEG/EEG signals. This is not currently possible with HNN’s neocortical model which supports more simplified models of pyramidal neuron dendrites. In summary, porting HNN's neocortical model into the NetPyNE format will add flexibility to the HNN software and help us optimally achieve our goals of including multiple interacting cortical and thalamic areas within our software.


Sunday, 1 July 2018 to Sunday, 30 June 2019

Funding source

NIH NIBIB (Subaward via Brown University)

Principal Investigator(s)

Salvador Dura-Bernal


Salvador Dura-Bernal (